SILVICULTURAL TASKS SCHEDULING OPTIMIZATION: A CASE STUDY OF FUNCTIONS AND METHODS
Keywords:
Integer linear programming, Simulated annealing, Operational researchAbstract
Scheduling problems are tasks of the operational routine in companies, which demand an optimal solution to support the decision. However, these problems have not been frequently investigated in forestry science. Therefore, it was proposed to describe a mathematical formulation for silviculture optimization under scheduling restriction of the workforce /sequencing of tasks (SSRCMM). Seeking the most suitable method to solve this combinatorial problem, two strategies were compared: i) Integer Linear Programming (ILP) and ii) simulated annealing (SA). The main criteria to assess strategies’ performance were to provide feasible solutions at an acceptable processing time and final project cost. The instance approached is a real problem outlined in 32 stands and five silvicultural tasks scheduled within a 40-day deadline. Three objective functions were also tested, defining case studies (S) to attend to the recurring managers’ decisions by minimizing: S1 – project cost, S2 – makespan, and S3 – workforce usage. The results reveal a robust model to support the forest planner in operational-level tasks. The ILP achieved the optimal solution only for the minimization of the project cost (S1) due to the delay in processing time of the other case studies. Thus, the SA stands out as an efficient method to solve the SSRCMM by providing satisfactory solutions in a reduced time. All the objective functions fitted properly with their proposed goals. The makespan and workforce usage functions increased by US$1,820.29 (S2) and US$2,146.39 (S3) from the S1, respectively, to finish the project earlier and reduce the oscillation of workforce usage over the days. Facing these findings, it is suggested that future researchers incorporate other challenges in decision-making, involving a multi-objective formulation or methods to reveal new insights for forest management and planning.
Keywords: Integer linear programming; Simulated annealing; Operational research
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